Overview

Dataset statistics

Number of variables19
Number of observations8642
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 8081 distinct valuesHigh cardinality
artist has a high cardinality: 1542 distinct valuesHigh cardinality
uri has a high cardinality: 8625 distinct valuesHigh cardinality
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 2 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceability and 3 other fieldsHigh correlation
tempo is highly overall correlated with time_signatureHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
time_signature is highly overall correlated with tempoHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 1209 (14.0%) zerosZeros
instrumentalness has 2877 (33.3%) zerosZeros

Reproduction

Analysis started2022-11-29 22:51:00.747916
Analysis finished2022-11-29 22:51:18.352367
Duration17.6 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8081
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
God Rest Ye Merry, Gentlemen
 
6
The Twelve Days of Christmas
 
4
Monster Mash
 
4
Run, Run, Run
 
4
Overture
 
4
Other values (8076)
8620 

Length

Max length158
Median length118
Mean length21.520829
Min length2

Characters and Unicode

Total characters185983
Distinct characters123
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7601 ?
Unique (%)88.0%

Sample

1st rowJealous Kind Of Fella
2nd rowInitials B.B.
3rd rowMelody Twist
4th rowMi Bomba Sonó
5th rowUravu Solla

Common Values

ValueCountFrequency (%)
God Rest Ye Merry, Gentlemen 6
 
0.1%
The Twelve Days of Christmas 4
 
< 0.1%
Monster Mash 4
 
< 0.1%
Run, Run, Run 4
 
< 0.1%
Overture 4
 
< 0.1%
Main Title 4
 
< 0.1%
Danny Boy 4
 
< 0.1%
Unchained Melody 4
 
< 0.1%
Goodnight My Love 4
 
< 0.1%
Just A Little Bit 4
 
< 0.1%
Other values (8071) 8600
99.5%

Length

2022-11-29T17:51:18.448550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1216
 
3.4%
850
 
2.4%
i 651
 
1.8%
a 644
 
1.8%
you 572
 
1.6%
in 492
 
1.4%
of 482
 
1.4%
love 463
 
1.3%
me 402
 
1.1%
to 386
 
1.1%
Other values (8002) 29187
82.6%

Most occurring characters

ValueCountFrequency (%)
26703
 
14.4%
e 16768
 
9.0%
o 11738
 
6.3%
a 11671
 
6.3%
n 9417
 
5.1%
i 8685
 
4.7%
r 8512
 
4.6%
t 7642
 
4.1%
l 6010
 
3.2%
s 5721
 
3.1%
Other values (113) 73116
39.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118892
63.9%
Uppercase Letter 31571
 
17.0%
Space Separator 26703
 
14.4%
Other Punctuation 4353
 
2.3%
Decimal Number 1741
 
0.9%
Dash Punctuation 970
 
0.5%
Open Punctuation 872
 
0.5%
Close Punctuation 871
 
0.5%
Final Punctuation 6
 
< 0.1%
Other Letter 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16768
14.1%
o 11738
9.9%
a 11671
9.8%
n 9417
 
7.9%
i 8685
 
7.3%
r 8512
 
7.2%
t 7642
 
6.4%
l 6010
 
5.1%
s 5721
 
4.8%
u 4556
 
3.8%
Other values (43) 28172
23.7%
Uppercase Letter
ValueCountFrequency (%)
T 3115
 
9.9%
M 2435
 
7.7%
S 2386
 
7.6%
I 2322
 
7.4%
A 2317
 
7.3%
L 2024
 
6.4%
B 1754
 
5.6%
C 1538
 
4.9%
D 1444
 
4.6%
W 1319
 
4.2%
Other values (24) 10917
34.6%
Other Punctuation
ValueCountFrequency (%)
' 1397
32.1%
, 973
22.4%
: 610
14.0%
. 593
13.6%
" 321
 
7.4%
/ 169
 
3.9%
! 144
 
3.3%
? 81
 
1.9%
& 41
 
0.9%
# 10
 
0.2%
Other values (4) 14
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 371
21.3%
2 301
17.3%
9 232
13.3%
0 228
13.1%
6 146
 
8.4%
7 115
 
6.6%
8 102
 
5.9%
4 92
 
5.3%
3 81
 
4.7%
5 73
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 969
99.9%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 835
95.8%
[ 37
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 834
95.8%
] 37
 
4.2%
Final Punctuation
ValueCountFrequency (%)
4
66.7%
2
33.3%
Space Separator
ValueCountFrequency (%)
26703
100.0%
Other Letter
ValueCountFrequency (%)
º 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Control
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 150465
80.9%
Common 35518
 
19.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16768
 
11.1%
o 11738
 
7.8%
a 11671
 
7.8%
n 9417
 
6.3%
i 8685
 
5.8%
r 8512
 
5.7%
t 7642
 
5.1%
l 6010
 
4.0%
s 5721
 
3.8%
u 4556
 
3.0%
Other values (78) 59745
39.7%
Common
ValueCountFrequency (%)
26703
75.2%
' 1397
 
3.9%
, 973
 
2.7%
- 969
 
2.7%
( 835
 
2.4%
) 834
 
2.3%
: 610
 
1.7%
. 593
 
1.7%
1 371
 
1.0%
" 321
 
0.9%
Other values (25) 1912
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185214
99.6%
None 756
 
0.4%
Punctuation 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26703
 
14.4%
e 16768
 
9.1%
o 11738
 
6.3%
a 11671
 
6.3%
n 9417
 
5.1%
i 8685
 
4.7%
r 8512
 
4.6%
t 7642
 
4.1%
l 6010
 
3.2%
s 5721
 
3.1%
Other values (70) 72347
39.1%
None
ValueCountFrequency (%)
é 166
22.0%
ó 91
12.0%
ã 74
9.8%
ç 61
 
8.1%
í 44
 
5.8%
á 42
 
5.6%
è 36
 
4.8%
ê 31
 
4.1%
ñ 29
 
3.8%
É 25
 
3.3%
Other values (29) 157
20.8%
Punctuation
ValueCountFrequency (%)
6
46.2%
4
30.8%
2
 
15.4%
1
 
7.7%

artist
Categorical

Distinct1542
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
Traditional
 
167
P. Susheela
 
130
Jerry Goldsmith
 
119
Harry Belafonte
 
117
Ennio Morricone
 
97
Other values (1537)
8012 

Length

Max length42
Median length33
Mean length13.762092
Min length3

Characters and Unicode

Total characters118932
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique640 ?
Unique (%)7.4%

Sample

1st rowGarland Green
2nd rowSerge Gainsbourg
3rd rowLord Melody
4th rowCelia Cruz
5th rowP. Susheela

Common Values

ValueCountFrequency (%)
Traditional 167
 
1.9%
P. Susheela 130
 
1.5%
Jerry Goldsmith 119
 
1.4%
Harry Belafonte 117
 
1.4%
Ennio Morricone 97
 
1.1%
Javier Solís 77
 
0.9%
Frank Zappa 77
 
0.9%
Raimon 77
 
0.9%
Giacomo Puccini 73
 
0.8%
Antônio Carlos Jobim 73
 
0.8%
Other values (1532) 7635
88.3%

Length

2022-11-29T17:51:18.571087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 1605
 
8.3%
331
 
1.7%
jerry 173
 
0.9%
johnny 172
 
0.9%
traditional 167
 
0.9%
bobby 162
 
0.8%
orchestra 131
 
0.7%
susheela 130
 
0.7%
p 130
 
0.7%
goldsmith 119
 
0.6%
Other values (2064) 16150
83.8%

Most occurring characters

ValueCountFrequency (%)
e 11981
 
10.1%
10628
 
8.9%
a 8830
 
7.4%
n 8063
 
6.8%
o 7804
 
6.6%
r 7764
 
6.5%
i 7022
 
5.9%
s 5094
 
4.3%
l 5041
 
4.2%
t 4479
 
3.8%
Other values (66) 42226
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88434
74.4%
Uppercase Letter 19046
 
16.0%
Space Separator 10628
 
8.9%
Other Punctuation 724
 
0.6%
Dash Punctuation 60
 
0.1%
Decimal Number 40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11981
13.5%
a 8830
10.0%
n 8063
9.1%
o 7804
8.8%
r 7764
8.8%
i 7022
 
7.9%
s 5094
 
5.8%
l 5041
 
5.7%
t 4479
 
5.1%
h 3893
 
4.4%
Other values (29) 18463
20.9%
Uppercase Letter
ValueCountFrequency (%)
T 2459
12.9%
B 1738
 
9.1%
S 1605
 
8.4%
J 1403
 
7.4%
C 1217
 
6.4%
M 1179
 
6.2%
G 1107
 
5.8%
D 951
 
5.0%
L 891
 
4.7%
R 868
 
4.6%
Other values (16) 5628
29.5%
Other Punctuation
ValueCountFrequency (%)
& 331
45.7%
. 281
38.8%
' 71
 
9.8%
" 28
 
3.9%
, 13
 
1.8%
Decimal Number
ValueCountFrequency (%)
6 14
35.0%
5 13
32.5%
4 12
30.0%
2 1
 
2.5%
Space Separator
ValueCountFrequency (%)
10628
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 107480
90.4%
Common 11452
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11981
 
11.1%
a 8830
 
8.2%
n 8063
 
7.5%
o 7804
 
7.3%
r 7764
 
7.2%
i 7022
 
6.5%
s 5094
 
4.7%
l 5041
 
4.7%
t 4479
 
4.2%
h 3893
 
3.6%
Other values (55) 37509
34.9%
Common
ValueCountFrequency (%)
10628
92.8%
& 331
 
2.9%
. 281
 
2.5%
' 71
 
0.6%
- 60
 
0.5%
" 28
 
0.2%
6 14
 
0.1%
5 13
 
0.1%
, 13
 
0.1%
4 12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118354
99.5%
None 578
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11981
 
10.1%
10628
 
9.0%
a 8830
 
7.5%
n 8063
 
6.8%
o 7804
 
6.6%
r 7764
 
6.6%
i 7022
 
5.9%
s 5094
 
4.3%
l 5041
 
4.3%
t 4479
 
3.8%
Other values (53) 41648
35.2%
None
ValueCountFrequency (%)
é 116
20.1%
á 91
15.7%
í 83
14.4%
ç 75
13.0%
ô 73
12.6%
ê 38
 
6.6%
ü 26
 
4.5%
ã 20
 
3.5%
ó 17
 
2.9%
ñ 14
 
2.4%
Other values (3) 25
 
4.3%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct8625
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
spotify:track:3tvqPPpXyIgKrm4PR9HCf0
 
2
spotify:track:1zU2N6UlqvEHfwL6VjNLHf
 
2
spotify:track:4Sz8zFchpHQVQVqVyGz5Jb
 
2
spotify:track:7l9DlqEWqgKVDIzWIeoObf
 
2
spotify:track:1uzcPl4GZSU9Ysl1ZcMLTb
 
2
Other values (8620)
8632 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters311112
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8608 ?
Unique (%)99.6%

Sample

1st rowspotify:track:1dtKN6wwlolkM8XZy2y9C1
2nd rowspotify:track:5hjsmSnUefdUqzsDogisiX
3rd rowspotify:track:6uk8tI6pwxxdVTNlNOJeJh
4th rowspotify:track:7aNjMJ05FvUXACPWZ7yJmv
5th rowspotify:track:1rQ0clvgkzWr001POOPJWx

Common Values

ValueCountFrequency (%)
spotify:track:3tvqPPpXyIgKrm4PR9HCf0 2
 
< 0.1%
spotify:track:1zU2N6UlqvEHfwL6VjNLHf 2
 
< 0.1%
spotify:track:4Sz8zFchpHQVQVqVyGz5Jb 2
 
< 0.1%
spotify:track:7l9DlqEWqgKVDIzWIeoObf 2
 
< 0.1%
spotify:track:1uzcPl4GZSU9Ysl1ZcMLTb 2
 
< 0.1%
spotify:track:7IS9MwiLJp91PEyoUDazqb 2
 
< 0.1%
spotify:track:0wz1LjDb9ZNEYwOmDJ3Q4b 2
 
< 0.1%
spotify:track:0hA8G8smCwi1h1nmxyRqT3 2
 
< 0.1%
spotify:track:3Jw2A9SC8zhntx4ON9VabX 2
 
< 0.1%
spotify:track:602rnDrA59nfIEcX5Qrlcx 2
 
< 0.1%
Other values (8615) 8622
99.8%

Length

2022-11-29T17:51:18.662218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:3tvqpppxyigkrm4pr9hcf0 2
 
< 0.1%
spotify:track:602rndra59nfiecx5qrlcx 2
 
< 0.1%
spotify:track:1zu2n6ulqvehfwl6vjnlhf 2
 
< 0.1%
spotify:track:30y2tmgeufdiux6pm5ncjn 2
 
< 0.1%
spotify:track:4mfu8kcloqd9nv03gfvrkn 2
 
< 0.1%
spotify:track:4ukylqjhdjt6mda22ofxum 2
 
< 0.1%
spotify:track:2zf8ro2hx0aeyaqxqdekw1 2
 
< 0.1%
spotify:track:4ucxtna6c5vcw79piz38vx 2
 
< 0.1%
spotify:track:602fffjeffsjnwc8ehdiwv 2
 
< 0.1%
spotify:track:4mrhemgssbtabx8ibnce1b 2
 
< 0.1%
Other values (8615) 8622
99.8%

Most occurring characters

ValueCountFrequency (%)
t 20126
 
6.5%
: 17284
 
5.6%
r 11661
 
3.7%
k 11635
 
3.7%
c 11601
 
3.7%
p 11595
 
3.7%
i 11587
 
3.7%
f 11557
 
3.7%
a 11541
 
3.7%
s 11528
 
3.7%
Other values (53) 180997
58.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 179982
57.9%
Uppercase Letter 75670
24.3%
Decimal Number 38176
 
12.3%
Other Punctuation 17284
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 20126
 
11.2%
r 11661
 
6.5%
k 11635
 
6.5%
c 11601
 
6.4%
p 11595
 
6.4%
i 11587
 
6.4%
f 11557
 
6.4%
a 11541
 
6.4%
s 11528
 
6.4%
o 11524
 
6.4%
Other values (16) 55627
30.9%
Uppercase Letter
ValueCountFrequency (%)
I 3067
 
4.1%
H 3026
 
4.0%
E 2954
 
3.9%
L 2950
 
3.9%
U 2946
 
3.9%
D 2939
 
3.9%
Q 2930
 
3.9%
X 2929
 
3.9%
K 2923
 
3.9%
G 2921
 
3.9%
Other values (16) 46085
60.9%
Decimal Number
ValueCountFrequency (%)
1 4161
10.9%
6 4115
10.8%
5 4037
10.6%
2 4014
10.5%
3 4010
10.5%
0 3994
10.5%
4 3984
10.4%
7 3857
10.1%
9 3043
8.0%
8 2961
7.8%
Other Punctuation
ValueCountFrequency (%)
: 17284
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 255652
82.2%
Common 55460
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 20126
 
7.9%
r 11661
 
4.6%
k 11635
 
4.6%
c 11601
 
4.5%
p 11595
 
4.5%
i 11587
 
4.5%
f 11557
 
4.5%
a 11541
 
4.5%
s 11528
 
4.5%
o 11524
 
4.5%
Other values (42) 131297
51.4%
Common
ValueCountFrequency (%)
: 17284
31.2%
1 4161
 
7.5%
6 4115
 
7.4%
5 4037
 
7.3%
2 4014
 
7.2%
3 4010
 
7.2%
0 3994
 
7.2%
4 3984
 
7.2%
7 3857
 
7.0%
9 3043
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 20126
 
6.5%
: 17284
 
5.6%
r 11661
 
3.7%
k 11635
 
3.7%
c 11601
 
3.7%
p 11595
 
3.7%
i 11587
 
3.7%
f 11557
 
3.7%
a 11541
 
3.7%
s 11528
 
3.7%
Other values (53) 180997
58.2%

danceability
Real number (ℝ)

Distinct808
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49216899
Minimum0
Maximum0.922
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:18.745527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.206
Q10.382
median0.501
Q30.612
95-th percentile0.743
Maximum0.922
Range0.922
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.16217857
Coefficient of variation (CV)0.32951807
Kurtosis-0.4724475
Mean0.49216899
Median Absolute Deviation (MAD)0.115
Skewness-0.1848209
Sum4253.3244
Variance0.02630189
MonotonicityNot monotonic
2022-11-29T17:51:18.855658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.477 31
 
0.4%
0.463 29
 
0.3%
0.534 28
 
0.3%
0.515 28
 
0.3%
0.65 28
 
0.3%
0.559 28
 
0.3%
0.6 28
 
0.3%
0.502 27
 
0.3%
0.418 27
 
0.3%
0.567 27
 
0.3%
Other values (798) 8361
96.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.0593 1
< 0.1%
0.0609 1
< 0.1%
0.0623 1
< 0.1%
0.0639 1
< 0.1%
0.0642 1
< 0.1%
0.0653 1
< 0.1%
0.0666 1
< 0.1%
0.0695 1
< 0.1%
0.0725 1
< 0.1%
ValueCountFrequency (%)
0.922 1
< 0.1%
0.918 1
< 0.1%
0.915 1
< 0.1%
0.914 1
< 0.1%
0.912 2
< 0.1%
0.899 2
< 0.1%
0.898 1
< 0.1%
0.895 2
< 0.1%
0.893 1
< 0.1%
0.89 2
< 0.1%

energy
Real number (ℝ)

Distinct1253
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44520962
Minimum0.000576
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:18.978721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000576
5-th percentile0.0921
Q10.281
median0.436
Q30.608
95-th percentile0.822
Maximum0.995
Range0.994424
Interquartile range (IQR)0.327

Descriptive statistics

Standard deviation0.2201721
Coefficient of variation (CV)0.4945358
Kurtosis-0.71018764
Mean0.44520962
Median Absolute Deviation (MAD)0.163
Skewness0.13691875
Sum3847.5015
Variance0.048475752
MonotonicityNot monotonic
2022-11-29T17:51:19.090366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.364 26
 
0.3%
0.43 24
 
0.3%
0.369 23
 
0.3%
0.458 23
 
0.3%
0.577 23
 
0.3%
0.341 23
 
0.3%
0.293 22
 
0.3%
0.456 22
 
0.3%
0.337 22
 
0.3%
0.402 22
 
0.3%
Other values (1243) 8412
97.3%
ValueCountFrequency (%)
0.000576 1
< 0.1%
0.000628 1
< 0.1%
0.00246 1
< 0.1%
0.00274 1
< 0.1%
0.00322 1
< 0.1%
0.00345 1
< 0.1%
0.0035 1
< 0.1%
0.00358 1
< 0.1%
0.00391 1
< 0.1%
0.00393 1
< 0.1%
ValueCountFrequency (%)
0.995 2
< 0.1%
0.991 1
< 0.1%
0.989 1
< 0.1%
0.988 1
< 0.1%
0.986 2
< 0.1%
0.985 1
< 0.1%
0.984 1
< 0.1%
0.982 1
< 0.1%
0.98 1
< 0.1%
0.972 1
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0764869
Minimum0
Maximum11
Zeros1209
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:19.191362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4500828
Coefficient of variation (CV)0.67962016
Kurtosis-1.238094
Mean5.0764869
Median Absolute Deviation (MAD)3
Skewness0.017910188
Sum43871
Variance11.903072
MonotonicityNot monotonic
2022-11-29T17:51:19.270342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1209
14.0%
7 1124
13.0%
5 1004
11.6%
2 955
11.1%
9 852
9.9%
4 678
7.8%
10 664
7.7%
8 516
6.0%
1 512
5.9%
3 425
 
4.9%
Other values (2) 703
8.1%
ValueCountFrequency (%)
0 1209
14.0%
1 512
5.9%
2 955
11.1%
3 425
 
4.9%
4 678
7.8%
5 1004
11.6%
6 319
 
3.7%
7 1124
13.0%
8 516
6.0%
9 852
9.9%
ValueCountFrequency (%)
11 384
 
4.4%
10 664
7.7%
9 852
9.9%
8 516
6.0%
7 1124
13.0%
6 319
 
3.7%
5 1004
11.6%
4 678
7.8%
3 425
 
4.9%
2 955
11.1%

loudness
Real number (ℝ)

Distinct6623
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-12.029726
Minimum-41.643
Maximum-0.507
Zeros0
Zeros (%)0.0%
Negative8642
Negative (%)100.0%
Memory size67.6 KiB
2022-11-29T17:51:19.363086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-41.643
5-th percentile-21.5478
Q1-14.39625
median-11.203
Q3-8.61125
95-th percentile-5.6531
Maximum-0.507
Range41.136
Interquartile range (IQR)5.785

Descriptive statistics

Standard deviation4.9936753
Coefficient of variation (CV)-0.4151113
Kurtosis2.6197448
Mean-12.029726
Median Absolute Deviation (MAD)2.8225
Skewness-1.2282877
Sum-103960.9
Variance24.936793
MonotonicityNot monotonic
2022-11-29T17:51:19.464839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12.349 7
 
0.1%
-11.804 6
 
0.1%
-11.743 5
 
0.1%
-13.869 5
 
0.1%
-9.105 5
 
0.1%
-7.91 5
 
0.1%
-8.429 5
 
0.1%
-12.269 5
 
0.1%
-8.958 4
 
< 0.1%
-10.072 4
 
< 0.1%
Other values (6613) 8591
99.4%
ValueCountFrequency (%)
-41.643 1
< 0.1%
-40.792 1
< 0.1%
-40.051 1
< 0.1%
-39.833 1
< 0.1%
-39.609 1
< 0.1%
-39.141 2
< 0.1%
-39.003 1
< 0.1%
-38.231 1
< 0.1%
-37.56 1
< 0.1%
-37.264 1
< 0.1%
ValueCountFrequency (%)
-0.507 1
< 0.1%
-0.81 1
< 0.1%
-0.934 1
< 0.1%
-1.101 1
< 0.1%
-1.144 1
< 0.1%
-1.155 1
< 0.1%
-1.17 1
< 0.1%
-1.348 1
< 0.1%
-1.864 1
< 0.1%
-1.904 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
1
6530 
0
2112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

Length

2022-11-29T17:51:19.565856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:19.656503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

Most occurring characters

ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8642
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8642
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6530
75.6%
0 2112
 
24.4%

speechiness
Real number (ℝ)

Distinct1025
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062402129
Minimum0
Maximum0.96
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:19.748208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0277
Q10.0323
median0.0387
Q30.0529
95-th percentile0.151
Maximum0.96
Range0.96
Interquartile range (IQR)0.0206

Descriptive statistics

Standard deviation0.098392959
Coefficient of variation (CV)1.5767564
Kurtosis50.028492
Mean0.062402129
Median Absolute Deviation (MAD)0.008
Skewness6.66624
Sum539.2792
Variance0.0096811744
MonotonicityNot monotonic
2022-11-29T17:51:19.858835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0307 56
 
0.6%
0.0339 55
 
0.6%
0.0306 52
 
0.6%
0.0304 50
 
0.6%
0.0321 49
 
0.6%
0.0315 48
 
0.6%
0.0313 47
 
0.5%
0.0319 46
 
0.5%
0.0295 46
 
0.5%
0.0314 46
 
0.5%
Other values (1015) 8147
94.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.0232 1
 
< 0.1%
0.0233 1
 
< 0.1%
0.0238 2
 
< 0.1%
0.0239 1
 
< 0.1%
0.0241 2
 
< 0.1%
0.0242 1
 
< 0.1%
0.0243 2
 
< 0.1%
0.0244 6
0.1%
0.0245 6
0.1%
ValueCountFrequency (%)
0.96 1
< 0.1%
0.957 1
< 0.1%
0.955 1
< 0.1%
0.954 1
< 0.1%
0.952 1
< 0.1%
0.951 1
< 0.1%
0.95 1
< 0.1%
0.949 1
< 0.1%
0.948 2
< 0.1%
0.946 2
< 0.1%

acousticness
Real number (ℝ)

Distinct1375
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61555679
Minimum5.38 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:19.979949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.38 × 10-6
5-th percentile0.065705
Q10.408
median0.683
Q30.851
95-th percentile0.977
Maximum0.996
Range0.99599462
Interquartile range (IQR)0.443

Descriptive statistics

Standard deviation0.28551509
Coefficient of variation (CV)0.46383224
Kurtosis-0.79534444
Mean0.61555679
Median Absolute Deviation (MAD)0.205
Skewness-0.57976981
Sum5319.6418
Variance0.081518864
MonotonicityNot monotonic
2022-11-29T17:51:20.081717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.98 37
 
0.4%
0.992 31
 
0.4%
0.994 29
 
0.3%
0.993 28
 
0.3%
0.991 27
 
0.3%
0.986 26
 
0.3%
0.99 25
 
0.3%
0.984 25
 
0.3%
0.978 25
 
0.3%
0.966 25
 
0.3%
Other values (1365) 8364
96.8%
ValueCountFrequency (%)
5.38 × 10-61
< 0.1%
2.82 × 10-51
< 0.1%
3.4 × 10-51
< 0.1%
4.87 × 10-51
< 0.1%
6.44 × 10-51
< 0.1%
7.58 × 10-51
< 0.1%
8.13 × 10-51
< 0.1%
8.67 × 10-51
< 0.1%
9.7 × 10-51
< 0.1%
0.000111 1
< 0.1%
ValueCountFrequency (%)
0.996 5
 
0.1%
0.995 22
0.3%
0.994 29
0.3%
0.993 28
0.3%
0.992 31
0.4%
0.991 27
0.3%
0.99 25
0.3%
0.989 22
0.3%
0.988 14
0.2%
0.987 16
0.2%

instrumentalness
Real number (ℝ)

Distinct2962
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14991921
Minimum0
Maximum0.999
Zeros2877
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:20.192890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.135 × 10-5
Q30.03655
95-th percentile0.898
Maximum0.999
Range0.999
Interquartile range (IQR)0.03655

Descriptive statistics

Standard deviation0.30205419
Coefficient of variation (CV)2.0147798
Kurtosis1.4948819
Mean0.14991921
Median Absolute Deviation (MAD)4.135 × 10-5
Skewness1.7878049
Sum1295.6018
Variance0.091236732
MonotonicityNot monotonic
2022-11-29T17:51:20.303946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2877
33.3%
0.9 15
 
0.2%
0.896 14
 
0.2%
0.911 11
 
0.1%
0.928 10
 
0.1%
0.867 9
 
0.1%
0.903 9
 
0.1%
0.885 9
 
0.1%
0.897 9
 
0.1%
0.858 9
 
0.1%
Other values (2952) 5670
65.6%
ValueCountFrequency (%)
0 2877
33.3%
1 × 10-62
 
< 0.1%
1.01 × 10-66
 
0.1%
1.02 × 10-62
 
< 0.1%
1.03 × 10-64
 
< 0.1%
1.04 × 10-65
 
0.1%
1.05 × 10-65
 
0.1%
1.06 × 10-63
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.08 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.999 1
< 0.1%
0.995 1
< 0.1%
0.994 1
< 0.1%
0.991 1
< 0.1%
0.989 2
< 0.1%
0.987 1
< 0.1%
0.986 2
< 0.1%
0.983 1
< 0.1%
0.982 1
< 0.1%
0.981 1
< 0.1%

liveness
Real number (ℝ)

Distinct1317
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21353077
Minimum0.0136
Maximum0.984
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:20.415099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0136
5-th percentile0.0626
Q10.103
median0.148
Q30.278
95-th percentile0.59395
Maximum0.984
Range0.9704
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.16946561
Coefficient of variation (CV)0.79363554
Kurtosis4.0464048
Mean0.21353077
Median Absolute Deviation (MAD)0.062
Skewness1.9385874
Sum1845.3329
Variance0.028718592
MonotonicityNot monotonic
2022-11-29T17:51:20.526555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.107 94
 
1.1%
0.11 84
 
1.0%
0.106 77
 
0.9%
0.108 73
 
0.8%
0.101 71
 
0.8%
0.114 71
 
0.8%
0.111 69
 
0.8%
0.113 63
 
0.7%
0.109 61
 
0.7%
0.121 61
 
0.7%
Other values (1307) 7918
91.6%
ValueCountFrequency (%)
0.0136 1
< 0.1%
0.0146 1
< 0.1%
0.0189 1
< 0.1%
0.0212 1
< 0.1%
0.0216 1
< 0.1%
0.0224 1
< 0.1%
0.023 1
< 0.1%
0.0241 1
< 0.1%
0.0248 1
< 0.1%
0.0267 1
< 0.1%
ValueCountFrequency (%)
0.984 1
 
< 0.1%
0.982 1
 
< 0.1%
0.973 1
 
< 0.1%
0.972 2
< 0.1%
0.971 3
< 0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.967 1
 
< 0.1%
0.966 3
< 0.1%
0.965 1
 
< 0.1%

valence
Real number (ℝ)

Distinct1206
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57496298
Minimum0
Maximum0.993
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:20.647609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.082115
Q10.361
median0.6025
Q30.816
95-th percentile0.961
Maximum0.993
Range0.993
Interquartile range (IQR)0.455

Descriptive statistics

Standard deviation0.27311135
Coefficient of variation (CV)0.47500684
Kurtosis-1.0186959
Mean0.57496298
Median Absolute Deviation (MAD)0.2245
Skewness-0.3124287
Sum4968.8301
Variance0.074589809
MonotonicityNot monotonic
2022-11-29T17:51:20.758605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 63
 
0.7%
0.963 52
 
0.6%
0.962 51
 
0.6%
0.965 49
 
0.6%
0.96 47
 
0.5%
0.964 42
 
0.5%
0.966 28
 
0.3%
0.967 25
 
0.3%
0.887 23
 
0.3%
0.901 20
 
0.2%
Other values (1196) 8242
95.4%
ValueCountFrequency (%)
0 7
0.1%
0.0203 1
 
< 0.1%
0.0245 1
 
< 0.1%
0.0256 1
 
< 0.1%
0.027 1
 
< 0.1%
0.0273 2
 
< 0.1%
0.0289 1
 
< 0.1%
0.0296 1
 
< 0.1%
0.0298 1
 
< 0.1%
0.03 1
 
< 0.1%
ValueCountFrequency (%)
0.993 1
 
< 0.1%
0.991 2
 
< 0.1%
0.984 1
 
< 0.1%
0.983 3
 
< 0.1%
0.982 2
 
< 0.1%
0.98 5
0.1%
0.979 1
 
< 0.1%
0.978 3
 
< 0.1%
0.977 9
0.1%
0.976 7
0.1%

tempo
Real number (ℝ)

Distinct8206
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.12527
Minimum0
Maximum241.009
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:20.877562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.5012
Q193.71825
median112.372
Q3131.91525
95-th percentile173.17525
Maximum241.009
Range241.009
Interquartile range (IQR)38.197

Descriptive statistics

Standard deviation29.21088
Coefficient of variation (CV)0.25373125
Kurtosis0.23825456
Mean115.12527
Median Absolute Deviation (MAD)19.046
Skewness0.60216411
Sum994912.61
Variance853.2755
MonotonicityNot monotonic
2022-11-29T17:51:20.989267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108.87 3
 
< 0.1%
79.981 3
 
< 0.1%
135.309 3
 
< 0.1%
101.307 3
 
< 0.1%
115.997 3
 
< 0.1%
66.588 3
 
< 0.1%
116.868 3
 
< 0.1%
144.338 3
 
< 0.1%
108.293 3
 
< 0.1%
111.003 3
 
< 0.1%
Other values (8196) 8612
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
31.988 1
< 0.1%
32.435 1
< 0.1%
34.333 1
< 0.1%
34.496 1
< 0.1%
36.52 1
< 0.1%
39.823 1
< 0.1%
45.053 1
< 0.1%
45.363 1
< 0.1%
46.315 1
< 0.1%
ValueCountFrequency (%)
241.009 1
< 0.1%
233.429 1
< 0.1%
212.9 1
< 0.1%
211.206 1
< 0.1%
210.874 1
< 0.1%
210.186 1
< 0.1%
209.864 1
< 0.1%
209.555 1
< 0.1%
209.482 1
< 0.1%
208.007 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct6360
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183638.34
Minimum15168
Maximum2516987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:21.100316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum15168
5-th percentile100293.7
Q1141070.25
median162746.5
Q3193230
95-th percentile334742.6
Maximum2516987
Range2501819
Interquartile range (IQR)52159.75

Descriptive statistics

Standard deviation100574.39
Coefficient of variation (CV)0.54767639
Kurtosis68.734758
Mean183638.34
Median Absolute Deviation (MAD)24573.5
Skewness5.8477416
Sum1.5870026 × 109
Variance1.0115207 × 1010
MonotonicityNot monotonic
2022-11-29T17:51:21.657072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158000 7
 
0.1%
155267 7
 
0.1%
164000 7
 
0.1%
162533 7
 
0.1%
139400 7
 
0.1%
162693 6
 
0.1%
143133 6
 
0.1%
154133 6
 
0.1%
157267 6
 
0.1%
151293 6
 
0.1%
Other values (6350) 8577
99.2%
ValueCountFrequency (%)
15168 1
< 0.1%
15629 1
< 0.1%
20573 1
< 0.1%
21587 1
< 0.1%
21950 1
< 0.1%
22215 1
< 0.1%
22957 1
< 0.1%
22989 1
< 0.1%
23400 1
< 0.1%
24183 1
< 0.1%
ValueCountFrequency (%)
2516987 1
< 0.1%
1637889 1
< 0.1%
1564400 1
< 0.1%
1478193 1
< 0.1%
1418213 1
< 0.1%
1336726 1
< 0.1%
1298000 1
< 0.1%
1240200 1
< 0.1%
1228133 1
< 0.1%
1181067 1
< 0.1%

time_signature
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
4
6944 
3
1395 
5
 
180
1
 
122
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8642
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

Length

2022-11-29T17:51:21.798879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:21.919909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8642
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8642
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6944
80.4%
3 1395
 
16.1%
5 180
 
2.1%
1 122
 
1.4%
0 1
 
< 0.1%

chorus_hit
Real number (ℝ)

Distinct8525
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.938212
Minimum0
Maximum187.49563
Zeros60
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:22.069508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.264874
Q127.094223
median35.159015
Q346.959238
95-th percentile72.367851
Maximum187.49563
Range187.49563
Interquartile range (IQR)19.865015

Descriptive statistics

Standard deviation17.496059
Coefficient of variation (CV)0.44932877
Kurtosis4.8181794
Mean38.938212
Median Absolute Deviation (MAD)9.253495
Skewness1.5606525
Sum336504.03
Variance306.11208
MonotonicityNot monotonic
2022-11-29T17:51:22.185412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
0.7%
54.06921 2
 
< 0.1%
27.53931 2
 
< 0.1%
31.95776 2
 
< 0.1%
65.96304 2
 
< 0.1%
25.6237 2
 
< 0.1%
31.53387 2
 
< 0.1%
57.0552 2
 
< 0.1%
37.11365 2
 
< 0.1%
51.61181 2
 
< 0.1%
Other values (8515) 8564
99.1%
ValueCountFrequency (%)
0 60
0.7%
4.14117 1
 
< 0.1%
4.1643 1
 
< 0.1%
5.37369 1
 
< 0.1%
5.58179 1
 
< 0.1%
5.69742 1
 
< 0.1%
7.74978 1
 
< 0.1%
7.8079 1
 
< 0.1%
7.95113 1
 
< 0.1%
7.96856 1
 
< 0.1%
ValueCountFrequency (%)
187.49563 1
< 0.1%
177.04593 1
< 0.1%
165.17163 1
< 0.1%
159.39547 1
< 0.1%
155.2747 1
< 0.1%
150.85993 1
< 0.1%
150.17626 1
< 0.1%
148.53513 1
< 0.1%
142.91811 1
< 0.1%
136.36078 1
< 0.1%

sections
Real number (ℝ)

Distinct56
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.884286
Minimum0
Maximum109
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size67.6 KiB
2022-11-29T17:51:22.301609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median8
Q310
95-th percentile15
Maximum109
Range109
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.329212
Coefficient of variation (CV)0.48728868
Kurtosis62.486736
Mean8.884286
Median Absolute Deviation (MAD)2
Skewness5.1318755
Sum76778
Variance18.742077
MonotonicityNot monotonic
2022-11-29T17:51:22.420519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 1494
17.3%
7 1391
16.1%
9 1253
14.5%
6 971
11.2%
10 950
11.0%
11 511
 
5.9%
5 482
 
5.6%
12 337
 
3.9%
4 231
 
2.7%
13 201
 
2.3%
Other values (46) 821
9.5%
ValueCountFrequency (%)
0 10
 
0.1%
1 1
 
< 0.1%
2 49
 
0.6%
3 131
 
1.5%
4 231
 
2.7%
5 482
 
5.6%
6 971
11.2%
7 1391
16.1%
8 1494
17.3%
9 1253
14.5%
ValueCountFrequency (%)
109 1
< 0.1%
76 1
< 0.1%
68 1
< 0.1%
63 1
< 0.1%
57 1
< 0.1%
55 1
< 0.1%
53 1
< 0.1%
50 1
< 0.1%
49 1
< 0.1%
46 2
< 0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
1
4321 
0
4321 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8642
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Length

2022-11-29T17:51:22.513777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:51:22.594733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Most occurring characters

ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8642
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8642
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4321
50.0%
0 4321
50.0%

Interactions

2022-11-29T17:51:16.866168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:02.865568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.968874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.188298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.291112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.378781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.626610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.731896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.850548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.123419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.257931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.392100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.516207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.950985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:02.943717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.053662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.272976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.360184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.463436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.708215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.816574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.935387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.207847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.342220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.476474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.616482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.035685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.028369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.131815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.357627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.453945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.541579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.798058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.916828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.019891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.308134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.426860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.561020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.694617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.120368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.113025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.216708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.435762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.538605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.626526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.876211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.994971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.104718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.386288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.511506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.645487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.779471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.205447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.197684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.301355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.520303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.623277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.711434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.960648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.079795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.182872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.470947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.611795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.730135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.864214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.299302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.282305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.386011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.604813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.707963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.795634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.045196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.164444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.267527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.555603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.689937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.814479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.948528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.383738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.366971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.486292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.689482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.792819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.880310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.130374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.249137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.367791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.640248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.774792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.914403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.048814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.468404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.451642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.570959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.774172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.861679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.098044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.215052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.333809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.452482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.740514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.859293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.992546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.133488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.549776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.545411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.655615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.858819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.955459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.188091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.299705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.418486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.537119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.825190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.959562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.077228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.218113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.637623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.630493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.733769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.936974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.040136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.269743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.393482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.503131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.621799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.909847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.044213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.177522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.512678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.722450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.715143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:04.818440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.021604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.124777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.353621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.478146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.596895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.869483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.987993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.113218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.262179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.602224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.822417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.799967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.018974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.106190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.209456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.444951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.562691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.681549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:11.954125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.091890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.213489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.346873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.693265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:17.922726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:03.884120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:05.103620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:06.206460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:07.294204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:08.533888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:09.662985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:10.766191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:12.038774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:13.173103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:14.313788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:15.431551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:51:16.781413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:51:22.686158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:51:22.848448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:51:23.001112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:51:23.163493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:51:23.305331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:51:23.407011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:51:18.054461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:51:18.250950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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